Exploring the Role of Immersive Virtual Reality Simulation in Health Professions Education: Thematic Analysis
Bibliographic record
Abstract
BACKGROUND: Although technology is rapidly advancing in immersive virtual reality (VR) simulation, there is a paucity of literature to guide its implementation into health professions education, and there are no described best practices for the development of this evolving technology. OBJECTIVE: We conducted a qualitative study using semi-structured interviews with early adopters of immersive VR simulation technology to investigate utilization and motivations behind employing this technology in educational practice, and to identify the educational needs that this technology can address. METHODS: We conducted 16 interviews with VR early adopters. Data were analyzed via Directed Content Analysis through the lens of the Unified Theory of Acceptance and Use of Technology (UTAUT). RESULTS: The main themes that emerged included Focus on Cognitive Skills, Access to Education, Resource Investment, and Balancing Immersion. These findings help to clarify the intended role of VR simulation in health professions education. Based on our data, we synthesize a set of research questions that may help define best practices for future VR development and implementation. CONCLUSIONS: Immersive VR simulation technology primarily serves to teach cognitive skills, to expand access to educational experiences, to act as a collaborative repository of widely relevant and diverse simulation scenarios, and to foster learning through deep immersion. By applying the UTAUT theoretical framework to the context of VR simulation, we not only collected validation evidence for this established theory, but also proposed several modifications to better explain use behavior in this specific setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".